tldr-code▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
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Token-efficient code analysis. 95% savings vs raw file reads.
TLDR-Code: Complete Reference
Token-efficient code analysis. 95% savings vs raw file reads.
Quick Reference
| Task | Command |
|---|---|
| File tree | tldr tree src/ |
| Code structure | tldr structure . --lang python |
| Search code | tldr search "pattern" . |
| Call graph | tldr calls src/ |
| Who calls X? | tldr impact func_name . |
| Control flow | tldr cfg file.py func |
| Data flow | tldr dfg file.py func |
| Program slice | tldr slice file.py func 42 |
| Dead code | tldr dead src/ |
| Architecture | tldr arch src/ |
| Imports | tldr imports file.py |
| Who imports X? | tldr importers module_name . |
| Affected tests | tldr change-impact --git |
| Type check | tldr diagnostics file.py |
| Semantic search | tldr semantic search "auth flow" |
The 5-Layer Stack
Layer 1: AST ~500 tokens Function signatures, imports
Layer 2: Call Graph +440 tokens What calls what (cross-file)
Layer 3: CFG +110 tokens Complexity, branches, loops
Layer 4: DFG +130 tokens Variable definitions/uses
Layer 5: PDG +150 tokens Dependencies, slicing
───────────────────────────────────────────────────────────────
Total: ~1,200 tokens vs 23,000 raw = 95% savings
CLI Commands
Navigation
# File tree
tldr tree [path]
tldr tree src/ --ext .py .ts # Filter extensions
tldr tree . --show-hidden # Include hidden files
# Code structure (codemaps)
tldr structure [path] --lang python
tldr structure src/ --max 100 # Max files to analyze
Search
# Text search
tldr search <pattern> [path]
tldr search "def process" src/
tldr search "class.*Error" . --ext .py
tldr search "TODO" . -C 3 # 3 lines context
tldr search "func" . --max 50 # Limit results
# Semantic search (natural language)
tldr semantic search "authentication flow"
tldr semantic search "error handling" --k 10
tldr semantic search "database queries" --expand # Include call graph
File Analysis
# Full file info
tldr extract <file>
tldr extract src/api.py
tldr extract src/api.py --class UserService # Filter to class
tldr extract src/api.py --function process # Filter to function
tldr extract src/api.py --method UserService.get # Filter to method
# Relevant context (follows call graph)
tldr context <entry> --project <path>
tldr context main --project src/ --depth 3
tldr context UserService.create --project . --lang typescript
Flow Analysis
# Control flow graph (complexity)
tldr cfg <file> <function>
tldr cfg src/processor.py process_data
# Returns: cyclomatic complexity, blocks, branches, loops
# Data flow graph (variable tracking)
tldr dfg <file> <function>
tldr dfg src/processor.py process_data
# Returns: where variables are defined, read, modified
# Program slice (what affects line X)
tldr slice <file> <function> <line>
tldr slice src/processor.py process_data 42
tldr slice src/processor.py process_data 42 --direction forward
tldr slice src/processor.py process_data 42 --var result
Codebase Analysis
# Build cross-file call graph
tldr calls [path]
tldr calls src/ --lang python
# Reverse call graph (who calls this function?)
tldr impact <func> [path]
tldr impact process_data src/ --depth 5
tldr impact authenticate . --file auth # Filter by file
# Find dead/unreachable code
tldr dead [path]
tldr dead src/ --entry main cli test_ # Specify entry points
tldr dead . --lang typescript
# Detect architectural layers
tldr arch [path]
tldr arch src/ --lang python
# Returns: entry layer, middle layer, leaf layer, circular deps
Import Analysis
# Parse imports from file
tldr imports <file>
tldr imports src/api.py
tldr imports src/api.ts --lang typescript
# Reverse import lookup (who imports this module?)
tldr importers <module> [path]
tldr importers datetime src/
tldr importers UserService . --lang typescript
Quality & Testing
# Type check + lint
tldr diagnostics <file|path>
tldr diagnostics src/api.py
tldr diagnostics . --project # Whole project
tldr diagnostics src/ --no-lint # Type check only
tldr diagnostics src/ --format text # Human-readable
# Find affected tests
tldr change-impact [files...]
tldr change-impact # Auto-detect (session/git)
tldr change-impact src/api.py # Explicit files
tldr change-impact --session # Session-modified files
tldr change-impact --git # Git diff files
tldr change-impact --git --git-base main # Diff against branch
tldr change-impact --run # Actually run affected tests
Caching
# Pre-build call graph cache
tldr warm <path>
tldr warm src/ --lang python
tldr warm . --background # Build in background
# Build semantic index (one-time)
tldr semantic index [path]
tldr semantic index . --lang python
tldr semantic index . --model all-MiniLM-L6-v2 # Smaller model (80MB)
Daemon (Faster Queries)
The daemon holds indexes in memory for instant repeated queries.
Daemon Commands
# Start daemon (backgrounds automatically)
tldr daemon start
tldr daemon start --project /path/to/project
# Check status
tldr daemon status
# Stop daemon
tldr daemon stop
# Send raw command
tldr daemon query ping
tldr daemon query status
# Notify file change (for hooks)
tldr daemon notify <file>
tldr daemon notify src/api.py
Daemon Features
| Feature | Description |
|---|---|
| Auto-shutdown | 30 minutes idle |
| Query caching | SalsaDB memoization |
| Content hashing | Skip unchanged files |
| Dirty tracking | Incremental re-indexing |
| Cross-platform | Unix sockets / Windows TCP |
Daemon Socket Protocol
Send JSON to socket, receive JSON response:
// Request
{"cmd": "search", "pattern": "process", "max_results": 10}
// Response
{"status": "ok", "results": [...]}
All 22 daemon commands:
ping, status, shutdown, search, extract, impact, dead, arch,
cfg, dfg, slice, calls, warm, semantic, tree, structure,
context, imports, importers, notify, diagnostics, change_impact
Semantic Search (P6)
Natural language code search using embeddings.
Setup
# Build index (downloads model on first run)
tldr semantic index .
# Default model: bge-large-en-v1.5 (1.3GB, best quality)
# Smaller model: all-MiniLM-L6-v2 (80MB, faster)
tldr semantic index . --model all-MiniLM-L6-v2
Search
tldr semantic search "authentication flow"
tldr semantic search "error handling patterns" --k 10
tldr semantic search "database connection" --expand # Follow call graph
Configuration
In .claude/settings.json:
{
"semantic_search": {
"enabled": true,
"auto_reindex_threshold": 20,
"model": "bge-large-en-v1.5"
}
}
Languages Supported
| Language | AST | Call Graph | CFG | DFG | PDG |
|---|---|---|---|---|---|
| Python | Yes | Yes | Yes | Yes | Yes |
| TypeScript | Yes | Yes | Yes | Yes | Yes |
| JavaScript | Yes | Yes | Yes | Yes | Yes |
| Go | Yes | Yes | Yes | Yes | Yes |
| Rust | Yes | Yes | Yes | Yes | Yes |
| Java | Yes | Yes | - | - | - |
| C/C++ | Yes | Yes | - | - | - |
| Ruby | Yes | - | - | - | - |
| PHP | Yes | - | - | - | - |
| Kotlin | Yes | - | - | - | - |
| Swift | Yes | - | - | - | - |
| C# | Yes | - | - | - | - |
| Scala | Yes | - | - | - | - |
| Lua | Yes | - | - | - | - |
| Elixir | Yes | - | - | - | - |
Ignore Patterns
TLDR respects .tldrignore (gitignore syntax):
# .tldrignore
.venv/
how to use tldr-codeHow to use tldr-code on Cursor
AI-first code editor with Composer
1Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add tldr-code
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill tldr-codeThe skills CLI fetches tldr-code from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
3Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?││ ── Universal (.agents/skills) ── always included ────│ • Amp│ • Antigravity│ • Cline│ • Codex│ ●Cursor(selected)│ • Cursor│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/tldr-codeReload or restart Cursor to activate tldr-code. Access the skill through slash commands (e.g., /tldr-code) or your agent's skill management interface.
⚠Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
✓Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
✓Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.6★★★★★53 reviews- ★★★★★Noor Diallo· Dec 16, 2024
tldr-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ama Malhotra· Dec 12, 2024
We added tldr-code from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yuki Okafor· Dec 8, 2024
Useful defaults in tldr-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Emma Zhang· Nov 7, 2024
We added tldr-code from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hiroshi Johnson· Nov 3, 2024
Useful defaults in tldr-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noor Abbas· Nov 3, 2024
tldr-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Noor Rahman· Oct 26, 2024
Solid pick for teams standardizing on skills: tldr-code is focused, and the summary matches what you get after install.
- ★★★★★Hiroshi Garcia· Oct 22, 2024
tldr-code has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Emma Liu· Oct 22, 2024
tldr-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Lucas Perez· Sep 5, 2024
I recommend tldr-code for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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